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I am trying to traverse through a collection, and update each document respectively.
My UserProfile collection consists of multiple JSON objects of userProfiles. As you can see, each profile has a lot of the same information. The only difference is the personal information. (This is just a test case of hard coded objects. The real data will be in an SQL DB managed by a sysadmin).
What I am trying to do is write a function (replaceTopics) that will take in an array of topics and replace each topic that matches in the collection. So if the system admin makes a change to a topic/s, he will send me the topic/s and I will be checking each document in my userProfile collection to see if that document has the matching topic (by matching topicIDs), if so, I need to replace that entire topic with the editedTopic.
I have tried this but with no luck. You can take a look at my function.
Related
I have a firestore collection with a bunch of documents, each with plenty subfields. On a web page I need a list of a specific subfields from each document.
Currently I load the the entire database when you load the page and then loop through and get the wanted values. This uses way to many reads to get very little data.
Is there a way to solve this e.g. a autogenerated a collection that contains field from other collection in an array or something.
Many thanks in advance
Auto-creating such a subcollection with just the fields you need is a great way to reduce the bandwidth needed to load the data.
There is nothing built into Firestore to create those derived documents, but it's fairly easy to build something using Cloud Functions. Create a function that responds to a Firestore onWrite trigger, and write the subset of the data to its destination there. It's common to have a separate Cloud Function for each such use-case, and I regularly see projects with 100+ such functions.
I expect we'll also start seeing Firebase Extensions for this type of thing, but right now no-one seems to have built one.
I want to write data into a specific location in the database. Let's say, I have a couple of users in the database. Each of them has their own personal information, including their e-mails. I want to find the user based on the e-mail, that's to say by using his e-mail (but I don't know exactly whose e-mail it is, but whoever it is do something with that user's information). To be more visible, here is my database sample.
Now, while working on one of my javascript files, when the user let's say name1 changes his name, I update my object in javascript and want to replace the whole object under ID "-LEp2F2fSDUt94SRU0cx". To cut short, I want to write this updated object in the path ("Users/-LEp2F2fSDUt94SRU0cx") without doing it by hand and just "knowing" the e-mail. So the logic is "Go find the user with the e-mail "name1#yahoo.com" and replace the whole object with his new updated object". I tried to use orderByChild("Email").equalTo("name1#yahoo.com").set(updated_object), but this syntax does not work I guess. Hopefully I could explain myself.
The first part is the query, that is separate from the post to update. This part is the query to get the value:
ref.child('users').orderByChild("Email").equalTo("name1#yahoo.com")
To update, you need to do something like this once you have the user id from the query result:
ref.child('users').child(userId).child("Email").update(newValue);
firebase.database.Query
A Query sorts and filters the data at a Database location so only a
subset of the child data is included. This can be used to order a
collection of data by some attribute (for example, height of
dinosaurs) as well as to restrict a large list of items (for example,
chat messages) down to a number suitable for synchronizing to the
client. Queries are created by chaining together one or more of the
filter methods defined here.
// Find all dinosaurs whose height is exactly 25 meters.
var ref = firebase.database().ref("dinosaurs");
ref.orderByChild("height").equalTo(25).on("child_added", function(snapshot) {
console.log(snapshot.key);
});
I am working on a project in Meteor which uses ElasticSearch as a search engine. I need the search feature on the site to allow 'stacking' searches. So, for instance, one can search for a file that a user in a certain 'group' uploaded by 'stacking' the user's name, followed by the group name and ending with the file name or some content in the file.
Now, on the MongoDB database the group, user, and files would be stored in separate collections and be related to each other through Ids. However, ElasticSearch uses a distributed datastore where everything is 'flat'. This makes it necessary to denormalize data/do application-side joins/etc. (https://www.elastic.co/guide/en/elasticsearch/guide/current/relations.html).
My question is: which method would be the best...
Denormalize data, use nests, etc.
--> So, when rivering data to the elasticsearch datastore, I would make copies of the data and replace every parent element with a new one which has the data added to it.
FOR EX. If someone comments on let's say a post in a group. The server would have to add to the general list of comments + find the post object, append the comment to it, and re-add the post object to the database + update the group object which contains the post object which should contain the comment + do the same for a user object (since I want to be able to stack searches on groups, users, etc.).
Basically When ever something is added or deleted, I'd have to update every object in the database that relates to it.
Run multiple elastic search queries (https://www.elastic.co/guide/en/elasticsearch/guide/current/application-joins.html) to retrieve the data I want.
Just perform search queries on each de-centralized collection, and use javascript on the server-side to compare the arrays and produce the search results.
** Note: this is for scaling up to a relatively mid-level load/usage. So around hundreds-thousands of instances of data to search through. Although, if this can work larger scale (millions), that would be great!
Please correct me if my understanding of anything is wrong, and thank you for reading through all this!
I'm using Meteor JS for a project so inherently I'm using MongoDB. I'm storing a user's check in and out actions. I'm currently storing them as individual docs in the collection. Each action contains 3 fields; in or out, time of action and userid. Is the best way to go though? Should I just have one doc per members and then store each action in an array? Is there another way? I anticipate several hundred members, but hopefully several thousands of members in the future. Thanks.
From experience, I can say that storing records instead of arrays is a better choice in the long run.
As far as Meteor is concerned, its reactivity handles collection records, but not individual fields in arrays. In other words, if one element gets added to the checkins array of a user object, the entire user object needs to be synchronized with the clients. If you store records instead, only the newly added record will be sent by the publication.
As far as MongoDB is concerned, there is a document size limit of 16MB. Not sure how frequent your checkins and checkouts are, but if you store them in an array, you might run into that limitation at some point.
Records are also easier to access than arrays.
For more details, see MongoDB data modeling and Database modeling in Bulletproof Meteor.
well, i am creating a network that allows users creating posts and like them.
Asking on stackoverflow i've understood how to structure my database:
A collection which includes a document for each post.
A collection which includes a document for each like, in each of these documents there is a reference to post is referenced to.
When i want to get ALL likes about a post i can query the like collection looking for the reference to that post.
And till here i am ok. But assuming i'll have millions documents in like collection, i wondered how could i query and search among them in not too long time.
And i was advised of ensureIndex, in this case, i have to ensureindex of the field which contains reference to a post.
But when do i have to create this index? is enough to create it once (for example when i set up my database) and it will be as default in mongodb or do i have to do it during application life-time? thank you
But assuming i'll have millions documents in like collection, i wondered how could i query and search among them in not too long time.
I assume you would most likely want to do a count on the likes as an example?
You can't, instead you use optimizations to combat this. A count on millions of rows might get a bit slow.
A typical scenario are counters in SQL techs that you use to amend the parent row with a sum figure of its children.
Same applies to MongoDB.
You would aggregate important data to the top.
If you require to actually query the likes to show some who have liked it then you limit those likes. Google+ and other networks tend to limit the amount of likes they show to about 1,000.
And i was advised of ensureIndex,
Adding indexes to a database does help with actually searching for documents.
But when do i have to create this index? is enough to create it once
Yes, MongoDB will manage the index itself. You only need to ensure it once.